Natural language processing (NLP) has made great strides in recent years in areas such as machine translation, speech processing, and search. “Question generation” refers to the act of automatically generating questions from source text documents, and is used for purposes such as training and educational testing. Question generation also encompasses the generation of answers. Though it can be applied in a wide variety of settings, the field of question generation has received relatively little attention. Present techniques for automated question generation have a number of limitations. For example, the generated questions may be limited to simple forms (e.g. similar to fill-in-the-blank), may not have a unique correct answer, or may not be well crafted (e.g. a person may readily distinguish that a particular question would not have been created by a person conversant with the language and subject matter). Often, alternatives to the correct answer are needed (e.g. in a multiple choice test or a true-false test); such alternatives are termed “distractors”. However, these “distractors” either may not be generated or may be of poor quality. Moreover, a set of generated questions may not provide good coverage of the source material when viewed collectively. Accordingly, there remains a need for improved natural language techniques for question generation and related problems.